Functional neuroimaging studies of the human brain have become increasingly important in the understanding of normal and pathological processes of cognition. Sophisticated statistical analytic frameworks have been developed to locate signal change and define brain networks involved in various tasks. However, in subtle cognitive impairment-e.g., exposure-related illness, early stages of degenerative diseases, injury, secondary illness following adjuvant therapy for cancer-these methods tend to have low sensitivity for detecting small changes in brain states resulting from mild brain dysfunction. An understanding of disease mechanism or progression of subtle cognitive dysfunction requires a novel statistical analytic framework with improved sensitivity to measure small changes in brain states. We have developed an innovative methodology that we successfully applied in measures of regional cerebral blood flow experiments. These methods use well established spatial modeling procedures, new to the functional brain imaging field, to derive statistically optimal spatial summaries within effective resolution groups or kriging, shown by preliminary studies to improve signal detection sensitivity and mitigate the multiple testing burden. Within this new spatial modeling framework, we propose to extend the kriging methodology to fMRI and EEG, modify existing techniques for characterizing brain networks of connectivity (e.g., kriging-based independent components analysis), and integrate the imaging modalities using a statistical classifier based on derived inputs of data driven effective resolution groups. Our primary goal is to develop this analysis framework to provide insight into the neurophysiological mechanisms of mild cognitive dysfunction. Achieving this goal may suggest treatments to alleviate symptoms, prevent progression, or at minimum, provide an informed clinical management of cognitively impaired patients.